Bibliographic record
Abstract
Translating picture books is a many-splendored thing: it includes not only the relationship between the verbal and the visual (images and other elements) but also issues like reading aloud and child images. In the following, while mainly concentrating on the visual, I will deal with the other questions as well, as they all interact and influence each other. My starting point is translating as rewriting for target-language audiences – we always need to ask the crucial question: “For whom?” Hence, while writing children’s books is writing for children, translating children’s literature is translating for children. (See Hunt 1990:1, 60-64 and Oittinen 2000.) The reasons why I take such a special interest in translating picture books are twofold: cultural and national as well as individual. In Finland, we translate a lot: 70-80% of all the books published for children annually are translations. From the perspective of picture books, the number may be even higher (and 90% of the translations come from the English language; see Rättyä 2002:18-23). Moreover, being an artist and translator of picture books makes me especially keen on the visual as a translation scholar as well. As a case study, I have chosen Maurice Sendak’s classical picture book Where the Wild Things Are and its translations into German, Swedish and Finnish. At the background of my article is my book Translating for Children (2000) as well as my forthcoming book Kuvakirja kääntäjän kädessä on translating picture books. Due to copyright reasons, I only have picture examples from illustrations of my own.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".